The first edition of this book was published in 2012, during a time when open source data analysis libraries for Python, especially pandas, were very new and developing rapidly. When the time came to write the second edition in 2016 and 2017, I needed to update the book not only for Python 3.6 (the first edition used Python 2.7) but also for the many changes in pandas that had occurred over the previous five years. Now in 2022, there are fewer Python language changes (we are now at Python 3.10, with 3.11 coming out at the end of 2022), but pandas has continued to evolve.

In this third edition, my goal is to bring the content up to date with current versions of Python, NumPy, pandas, and other projects, while also remaining relatively conservative about discussing newer Python projects that have appeared in the last few years. Since this book has become an important resource for many university courses and working professionals, I will try to avoid topics that are at risk of falling out of date within a year or two. That way paper copies won't be too difficult to follow in 2023 or 2024 or beyond.

A new feature of the third edition is the open access online version hosted on my website at https://wesmckinney.com/book, to serve as a resource and convenience for owners of the print and digital editions. I intend to keep the content reasonably up to date there, so if you own the paper book and run into something that doesn't work properly, you should check there for the latest content changes.

The 3rd edition of Python for Data Analysis is now available as an “Open Access” HTML version on this site https://wesmckinney.com/book in addition to the usual print and e-book formats. This edition was initially published in August 2022 and will have errata fixed periodically over the coming months and years. If you encounter any errata, please report them here.

In general, the content from this website may not be copied or reproduced. The code examples are MIT-licensed and can be found on GitHub or Gitee along with the supporting datasets.

If you find the online edition of the book useful, please consider ordering a paper copy or a DRM-free eBook (in PDF and EPUB formats) to support the author.

This web version of the book was created with the Quarto publishing system.

(Fonte : https://wesmckinney.com/)